Attention-based hybrid CNN-LSTM and spectral data augmentation for COVID-19 diagnosis from cough sound

Abstract COVID-19 pandemic has fueled the interest in artificial intelligence tools for quick diagnosis to limit virus spreading. Over 60% of people who are infected complain of a dry cough. Cough and other respiratory sounds were used to build diagnosis models in much recent research. We propose in this work, an augmentation pipeline which is applied on the pre-filtered data and uses i) pitch-shifting technique to augment the raw signal and, ii) spectral data augmentation technique SpecAugment to augment the computed mel-spectrograms. A deep learning based architecture that hybridizes convolution neural networks and long-short term memory with an attention mechanism is proposed for building the classification model. The feasibility of the proposed is demonstrated through a set of testing scenarios using the large-scale COUGHVID cough dataset and through a comparison with three baselines models. We have shown that our classification model achieved 91.13% of testing accuracy, 90.93% of sensitivity and an area under the curve of receiver operating characteristic of 91.13%

Gender identification from arabic speech using machine learning

Abstract Speech recognition is becoming increasingly used in real-world applications. One of the interesting applications is automatic gender recognition which aims to recognize male and female voices from short speech samples. This can be useful in applications such as automatic dialogue systems, system verification, prediction of demographic attributes (e.g., age, location) and estimating person’s emotional state. This paper focuses on gender identification from the publicly available dataset Arabic Natural Audio Dataset (ANAD) using an ensemble-classifier based approach. More specifically, initially we extended the original ANAD to include a gender label information through a manual annotation task. Next, in order to optimize the feature engineering process, a three stage machine learning approach is devised. In the first phase, re restricted to features to the two widely used ones; namely, MFCC and fundamental frequency coefficients. In the second phase, six distinct acoustic features were employed. Finally, in the third phase, the features were selected according to their associated weights in Random Forest Classifier, and the best features are thereby selected. The latter approach enabled us to achieve a classification rate of 96.02% on the test set generated with linear SVM classifier

Arabic dialects identification:North African dialects case study

Abstract Arabic is the fourth most used language on the Internet and the official language of more than 20 countries around the world. It has three main varieties, Modern Standard Arabic, which is used in books, news and education, local Dialects that vary from region to another, and Classical Arabic, the written language of the Quran. Maghrebi dialect is the Arabic dialect language used in North African countries, where internet users from these countries feel more comfortable using local slangs than native Arabic. In this study, we present a large dataset of regional dialects of three countries, namely Algeria, Tunisia, and Morocco, then we investigate the identification of each dialect using a machine learning classifiers with TF-IDF features. The approach shows promising results, where we achieved accuracy up to 96%

COVID-19 detection from Xray and CT scans using transfer learning

Abstract Since the novel coronavirus SARS-CoV-2 outbreak, intensive research has been conducted to find suitable tools for diagnosis and identifying infected people in order to take appropriate action. Chest imaging plays a significant role in this phase where CT and Xrays scans have proven to be effective in detecting COVID-19 within the lungs. In this research, we propose deep learning models using Transfer learning to detect COVID-19. Both X-ray and CT scans were considered to evaluate the proposed methods